Top 5 Tips for Local Businesses to Rule the Local SEO Competition!

More and more people using Google search are seeing more local results thrown up on the top. They are bombarded with local options for nearly everything they search especially when on a mobile device. Hence now it is more important for businesses with a local base targeting to target and top the local search metrics. Here we are listing top 5 Tips for local SEO to top local search results and boost their traffic and business.

1) Claim Your Online Listing: This is the first and the most important step. You need to claim your listing on Google Places, Bing Local and Yahoo Local. Once you fill out your details after creating an account on these platforms, you will need to verify your business details through a straight forward process of post card verification or a phone call. Once it is done you are ready for the next steps. If you don’t have a physical address on some location or don’t want people to know your real business address and have reason to keep it discreet you can use certain services which provide you with business mail forwarding and other services (please keep in mind that use of these services is in the grey area and should be avoided for all practical purposes.

2) Optimize your Listings: Putting in accurate and detailed business information such as address and phone number, is of utmost importance. Opening closing times and days of operation etc. are required. Description of your business activities is very important as well as using your keywords in the text as well. Use high quality images and videos of your business. This is of utmost importance as it plays a major role in enhancing user experience. Leaving any of the important field in the listings blank is not recommended. Accurate information that is optimized to help you rank well and also to attract your potential clients and impress them is the key to proper local SEO.

3) Online Business Reviews: Reviews are another important part of your local Listing. Reviews play a super crucial role in local SEO and most of the local listing sites are completely fine with businesses requesting their customers to leave a review. You should request your customers for writing a review through e-mails, invoices, contact form and thank you pages, among other places. Keep in mind any review is good for business, even negative ones. However keep in mind not to have too many bad reviews. No business is perfect and negative reviews are part of business life. Many businesses are afraid of bad reviews, but keep in mind no company can keep 100% of their customers happy. Another important thing to remember is that many potential customers may find it suspicious if a company only has positive reviews.

4) Optimizing your Website: Optimizing your website is also crucial. Display a local phone number prominently on your homepage and landing page in text format (don’t use image) and mention your local business address again in text format only. Make sure that the phone number is a real working local phone number with local dialing code mentioned and not an 1800/800 type of number. Also make sure to use the full mailing address of your business and make sure that the details are same as used on the listing and there is no difference. Make use of schema to optimize your website. Use multiple landing pages if your business caters to different geo locations. Optimize each page according to the targeted location.

5) Local Citations: It is important to list your business in the local directories and local classifieds sites. The search engine algorithms still depend on these to gather more information about the business and their popularity. So use sites such yelp, yellow pages etc. to list your business and help boost your local rankings.

These five top tips will help you in your endeavor to boost your online presence and help you beat the local SEO competition.

Tips For Establishing Business Credit Fast

Borrowing from the SBA

Borrowing money is one of the most common sources of funding for a small business, but obtaining a loan isn’t always easy. Before you approach your banker for a loan, it is a good idea to understand as much as you can about the factors the bank will evaluate when they consider your loan. This discussion outlines some of the key factors a bank uses to analyze a potential borrower. Also included is a self-assessment checklist at the end of this section for you to complete.

Key Points to Consider

Some of the key points your banker will review:

1. Ability/Capacity to Repay

The ability to repay must be justified in your loan package. Banks want to see two sources of repayment – cash flow from the business, plus a secondary source such as collateral. In order to analyze the cash flow of the business, the lender will review the business past financial statements. Generally, banks feel most comfortable dealing with a business that has been in existence for a number of years, as they have a financial track record. If the business has consistently made a profit and that profit can cover the payment of additional debt, then it is likely the loan will be approved. If, however, the business has been operating marginally and now has a new opportunity to grow, or if that business is a startup, then it is necessary to prepare a thorough loan package with a detailed explanation addressing how the business will be able to repay the loan.

2. Credit History

One of the first things a bank will determine when a person/business requests a loan is whether their personal and business credit is good. Therefore, before you go to the bank or even start the process of preparing a loan request, make sure your credit is good

3. Equity

Financial institutions want to see a certain amount of equity in a business. Equity can be built up through retained earnings or the injection of cash from either the owner or investors. Most banks want to see that the total liabilities or debt of a business is not more than 4 times the amount of equity. (Or, stated differently, when you divide total liabilities by equity, your answer should not be more than 4.) Therefore, if you want a loan, you must ensure that there is enough equity in the company to leverage that loan.

Don’t be misled into thinking that startup businesses can obtain 100% financing through conventional or special loan programs. A business owner usually must put some of his/her own money into it. The amount an individual must put into the business in order to obtain a loan is dependent on the type of loan, purpose, and terms. For example, most banks want the owner to put in at least 20 – 40% of the total request.

Example: A new business needs a $100,000 to start. The business owner must put $20,000 of his/her own money into the new business as equity. His/Her loan will be $80,000. The debt to equity ratio is 4:1. Note that this is only one of many factors used to evaluate the business – simply having the right debt to equity ratio does not guarantee you’ll get the loan.

The balance sheet indicates the amount of equity or net worth of a business. The net worth of the business is often a combination of retained earnings and the owner’s equity. In many cases, an owner’s equity will be shown as a loan from shareholders, and is therefore a liability. If a business owner wishes to obtain a loan, he/she will be obligated to pay the bank back first, not his/herself. Consequently, it may be necessary to restructure the liability so that it becomes the owner’s equity, or subordinate the loan. If the current debt to net worth is 4 or over, it is unlikely that the business will be able to obtain additional debt/loan. Understand your financial statements.

Understanding Financial Statements:

The primary financial statements are represented in the balance sheet and income statement. Learn more about these statements

BALANCE SHEET

The balance sheet is a snapshot of the company’s financial standing at an instant in time. The balance sheet shows the company’s financial position, what it owns (assets) and what it owes (liabilities and net worth). The “bottom line” of a balance sheet must always balance (i.e. assets = liabilities + net worth). The individual elements of a balance sheet change from day to day and reflect the activities of the company. Analyzing how the balance sheet changes over time will reveal important information about the company’s business trends.

INCOME STATEMENT

Known also as the profit and loss statement, the income statement shows all income and expense accounts over a period of time. That is, it shows how profitable the business is. This financial statement shows what how much money the company will make after all expenses are accounted for. Remember that an income statement does not reveal hidden problems like insufficient cash flow problems. Income statements are read from top to bottom and represent earnings and expenses over a period of time.

4. Collateral

Financial institutions are looking for a second source of repayment, which is often collateral. Collateral are those personal and business assets that can be sold to pay back the loan. Every loan program, even many microloan programs, requires at least some collateral to secure a loan. If a potential borrower has no collateral, he/she will need a co-signer that has collateral to pledge. Otherwise, it may be difficult to obtain a loan.

The value of collateral is not based on market value; that is discounted to take into account the value that would be lost if the assets had to be liquidated.

5. Experience

A client who wants to open a business and has no experience in that business should not seek financing, let alone start the business unless they intend to hire people who know the business or take on a partner that has the appropriate experience. Regardless, the client should be advised to take some time to work in the business first and take some entrepreneurial training classes.
Sample Collateral Chart [http://www.corporatefasttrack.com/SBA_Collateral_Ratio.htm]

Questions Your Banker Will Ask

The key questions the banker will be seeking to answer are as follows:

  1. Can the business repay the loan? (Is cash flow greater than debt service?)
  2. Can you repay the loan if the business fails? (Is collateral sufficient to repay the loan?)
  3. Does the business collect its bills?
  4. Does the business control its inventory?
  5. Does the business pay its bills?
  6. Are the officers committed to the business?
  7. Does the business have a profitable operating history?
  8. Does the business match its sources and uses of funds?
  9. Are sales growing?
  10. Does the business control expenses?
  11. Are profits increasing as a percentage of sales?
  12. Is there any discretionary cash flow?
  13. What is the future of the industry?
  14. Who is your competition and what are their strengths and weaknesses ?

Data Denial and Business Intelligence – How to Achieve Data Quality

The greatest battle you may face inside the organization will be to get management to the point where they agree that data quality is a goal even worth considering.

Everybody talks about data, but many often confuse it with information and knowledge. Basically, data is a core corporate asset that must be synthesized into information before it can serve as the basis for knowledge within the organization. Nevertheless, data is ubiquitous – it is used to support every aspect of the business, and is an integral component of every key business process. However, incorrect data cannot generate useful information, and knowledge built on invalid information can lead organizations into catastrophic situations. As such, the usefulness of the data is only as good as the data itself – and this is where many organizations run into trouble.

Many organizations neither recognize nor accept the bad quality status of their data, and try instead to divert the attention to supposed faults within their respective systems or processes. To these organizations data denial has practically become an art form, where particularly daunting corporate barriers have been built – typically over long periods of time – to avoid the call to embark on any “real” Data Quality improvement initiatives.

However, we have found that the best way to measure the extent to which your organization may be dealing with data denial is to ask the following key questions:

  • Are you aware of any Data Quality issues within your company?
  • Are there existing processes that are not working as originally designed?
  • Are people circumventing, the system in order to get their work completed?
  • Have you ever been forced to deny a business request for information due to an issue of Data Quality?
  • If the system was functioning properly, would this information have been readily available?
  • Has a business case been made outlining the economic impact of this issue? And, if so, has it ever been addressed with the organization’s leadership?
  • What was the response to these issues? And if there was no response, what is stifling this process?
  • What causes these “gaps” in Data Quality?
  • How are these issues affecting the responsiveness of your organization (i.e., to customers, stockholders, employees, etc.)?
  • If these issues were to be addressed and corrected, what strategic value would be added or enhanced?
  • Who bears the responsibility for addressing these issues within your organization?
  • What can be done to address these issues in the future?
  • What support is needed to implement a Data Quality strategy?

Depending on the answers to these questions, your organization may already be facing significant barriers to attaining Data Quality, each of which will need to be identified, assessed, prioritized and corrected. According to William K. Pollock, president of the Westtown, PA-based services consulting firm, Strategies For GrowthSM, “Most companies already know what data they do not have – and for them, this is a significant problem. However, the same companies are probably not aware that some of the data they do have may be faulty, incomplete or inaccurate – and if they use this faulty data to make important business decisions, that becomes an even bigger problem”.

Common Problems with Corporate Data

Research has shown that the amount of data and information acquired by companies has close to tripled in the past four years, while an estimated 10 to 30 percent of it may be categorized as being of “poor quality” (i.e., inaccurate, inconsistent, poorly formatted, entered incorrectly, etc.). The common problems with corporate data are many, but typically fall into the following five major areas:

  • Data Definition – typically manifesting itself through inconsistent definitions within a company’s corporate infrastructure.
  • Initial Data Entry – caused by incorrect values entered by employees (or vendors) into the corporate database; typos and/or intentional errors; poor training and/or monitoring of data input; poor data input templates; poor (or nonexistent) edits/proofs of data values; etc.
  • Decay – causing the data to become inaccurate over time (e.g., customer address, telephone, contact info; asset values; sales/purchase volumes; etc.).
  • Data Movement – caused by poor extract, transform and load (ETL) processes that lead to the creation of data warehouses often comprised of more inaccurate information than the original legacy sources, or excluding data that is mistakenly identified as inaccurate; inability to mine data in the source structure; or poor transformation of data.
  • Data Use – or the incorrect application of data to specific information objects, such as spreadsheets, queries, reports, portals, etc.

Each of these areas represents a potential problem to any business; both in their existence within the organization, as well as the ability of the organization to even recognize that the problem exists. In any case, these are classic symptoms of “data denial” – one of the most costly economic drains on the well-being of businesses today.

Data Quality Maturity Levels

There are five key status indicators that can be used to measure the existing levels of Data Quality maturity in an organization, each with its own set of distinct corporate – and human – attributes. However, it is at the mature level where you will want your organization to be positioned.

  1. Embryonic – this level is the least beneficial place to be, as Data Quality does not even appear on the organization’s radar screen; there is extensive finger-pointing with respect to data-associated blame, generally leading to cover-ups and CYAs; and there is no formal Data Quality organization in place. As far as the humans involved in the process are concerned – they are totally “clueless”.
  2. Infancy– this level is not that much better, although the organization has begun to consider looking into Data Quality; various ad hoc groups may have been established to search for “answers”; and Data Quality has been positioned as a subset of corporate IT. This typically occurs as the human element begins to show an emerging interest.
  3. Adolescence – this level is one of mixed Data Quality accomplishments where most of the pain points have already been identified and the strategy team has shifted into a crisis-driven “full court press” managed by formal Data Quality teams that are populated and coordinated by both IT and the Business. However, this is also the point where alternating periods of panic and frenzy typically set in.
  4. Young Adult – by the time the organization reaches this level, there begins to be some semblance of an evolving Data Quality structure, where the entire organization is involved; one where both IT and the Business have begun to work as partners toward a common goal. Accordingly, the human attribute has also become much more “stabilizing”.
  5. Mature – once the organization has attained the this level, it has finally reached the point where it has implemented an effective Data Quality structure, characterized by collaborative efforts and Data Quality/Center of Excellence (DQCE), as well as the ability to measure and track customer value over time. As such, the organization has been able to attain a “controlled” environment, where all of the personnel involved – on both the supply and demand sides – are comfortable that the desired levels of Data Quality have been achieved.

Moving Toward Data Quality

Data Quality is the desired state where an organization’s data assets reflect the following attributes:

  • Clear definition or meaning;
  • Correct values;
  • Understandable presentation format (as represented to a knowledge worker); and
  • Usefulness in supporting targeted business processes.

However, regardless of the state of the organization’s data assets, there must still be a balance of data, process and systems in order to meet the company’s stated business objectives, which generally focus on things like:

  • Increasing revenues and margins;
  • Increasing market share; and/or
  • Increasing customer satisfaction.

In today’s economy, companies tend to focus their investments more on packaged systems and business process optimization, rather than on Data Quality. As a result, investment in corporate Data Quality is often overlooked – and this can very easily lead to a significant reduction in the organization’s ability to effectively answer critical business questions, such as:

  • Who is our customer?
  • Are we missing sales opportunities?
  • Is the customer’s product entitled to service?
  • Are inaccuracies causing customer dissatisfaction?
  • What should we spare; how many; where?
  • Are our service functions efficient; is our decision support timely and reliable?
  • How is our product defined?
  • Is our billing accurate and timely?

The inability to answer these critical business questions leads to data quality issues such as:

  • Inconsistent or incomplete product structure and service data
  • Inability to uniquely identify entitled versus non-entitled equipment
  • Incomplete or non-existent configuration data on entitled products
  • Duplication and redundancy

But, it gets even worse! Poor Data Quality eventually stunts operational efficiency in virtually every area of the organization, as otherwise valuable resources (i.e., personnel, dollars, time, etc.) are required to spend an inordinate – and unnecessary – amount of extra effort:

  • Searching for missing data;
  • Correcting inaccurate information;
  • Creating temporary, or permanent, workarounds;
  • Laboring to assemble information from disparate data bases; and
  • Resolving data-related customer complaints.

Over time, poor data quality significantly decreases an organization’s revenue-generating opportunities. Lost revenue can exist is the following:

  • Lost Maintenance Contract Revenue – products that should be under contract are not captured and billing revenue is understated.
  • Lost T&M Revenue – Non-entitled products that should be serviced under T&M are serviced under contract
  • Lost Product Upgrade Opportunities – Inability to identify customer need for product and software upgrades
  • Incorrect Maintenance Charges – Incorrect contract pricing since product configurations cannot be accurately identified.
  • Lost Customer – Lost customers and revenue due to dissatisfaction with poor asset management and cumbersome reconciliations.
  • Delayed Contract Renewals – lost renewal revenue and increased admin costs due to delays in new contract initiation.
  • Overlooked Cross-sell & Up-sell Opportunities – missed opportunity to sell complementary or advanced solutions die to inaccurate records

Poor data quality also significantly increases its operating costs and, may in fact, lead to a reduction of customer satisfaction. Increased operating costs can exist in the following areas:

  • Sales Team – more time is required to manage new opportunities and create quotes, less time is spent selling and and quoting new maintenance contracts becomes inaccurate.
  • Customer Care Center – T&M billing disputes increase, the cost of contract dispute resolution is higher and there is a decreased accuracy and timeliness of invoices with increased dispute losses.
  • Contract Management – the effectiveness and timeliness of renewal activity is decreased.
  • Logistics – stocking locations become sub-optimized by an over/under stocking of spare parts.
  • Finance – data for decision support and performance reporting becomes incomplete and/or inaccurate.
  • Service Delivery – tech on-calls are doubled dispatched due to the wrong part, service level commitments are missed and trouble call handling is degraded.
  • Product Management – the product lifecycle position is inaccurately identified and inaccurate service history affects service offering decisions.
  • Services Marketing – the ability to develop pricing programs is hindered, marketing programs are not deployed effectively and there is an increased burden/time for data collection.

How to Achieve Data Quality

Arguably, the greatest battle you may face inside the organization will be to get management to the point where they agree that data quality is a goal even worth considering. To do this, every organization must have a champion to help find ways for removing barriers and changing existing perceptions. The primary focus of the champion should be on:

  • Assisting in making data quality a strategic priority;
  • Assuring that data quality will be used to enable business processes; and
  • Find – and communicate – compelling ways to make data quality attractive.

In our own experiences, Bardess Group has assisted many organizations to achieve data quality by applying the most effective methodology for accelerating the data cleansing and control processes.

Finding Success

Many organizations can achieve data quality by applying the most effective methodology for accelerating the data cleansing and control processes.

The seven major steps that must be taken to achieve Data Quality are:

  1. Acknowledge the problem, and identify the root causes;
  2. Determine the scope of the problem by prioritizing data importance and performing the necessary data assessments;
  3. Estimate the anticipated ROI, focusing on the difference between the cost of improving Data Quality vs. the cost of doing nothing;
  4. Establish a single owner of Data Quality with accountability (e.g., make it a senior management role, such as a Data Officer/DQ COE);
  5. Create a Data Quality vision and strategy, and identify the key change drivers;
  6. Develop a formal Data Quality improvement program based on specific tools wherever possible (e.g., First Logic, Trillium, IBM Ascential, Data Flux, Group 1), and use a value-driven approach for large projects; and
  7. Make it a priority to move your organization up through the levels of the Data Maturity model!

Achieving Data Quality is critical, but getting there is often a complex process. Data Quality requires commitments from all business functions, as well as from the top-down. Quick fixes typically do not work and generally only end up creating frustration. For many organizations, it may have taken years to create and foster a culture of data denial, and it will require rigorous processes to:

  • First, identify the problem before it can be fixed and;
  • Second, recognize – and accept – the full extent of the potential benefits that can ultimately be realized.

However, for many business enterprises, the numbers speak for themselves, where the implementation of a Data Quality initiative can ultimately lead to:

Reductions ranging from:

  • 10 – 20% of corporate budgets,
  • 40 – 50% of the IT budget, and
  • 40% of operating costs;
  • And increases of:
  • 15 – 20 % in revenues, and
  • 20 – 40% in sales

The application of Data Quality can provide an organization with the opportunity to capitalize on its cumulative information and knowledge assets. Knowledge that was previously unknown – or unavailable – such as cross-referenced customer buying patterns, profiles of potential buyers, or specific patterns of product/service usage may be uncovered and put into practical use for the first time. The end result can lead to anything ranging from improvements in operational efficiency, more accurate sales forecasting, more effective target marketing, and improved levels of customer service and support – all based on a strong foundation of Data Quality.